Building Types

This example demonstrates how to map dwelling units by primary rensidence status and structural type for primary residences. ## Data First we grab the building data via cancensus, making use of the CensusMapper API tool to select the regions and variables we need.

#devtools::install_github("mountainmath/cancensus")
library(cancensus)
library(dotdensity)
# options(cancensus.api_key='your_api_key')
dataset='CA16'
regions=list(CSD="3520005")
vector_data <- search_census_vectors("structural type of dwelling",dataset) %>%
  child_census_vectors(TRUE)
vectors <- vector_data %>%  pull(vector)

We choose the categories and colours we want to map and define a convenience function to rename the variables and compute the qantities for the other asian countries that we don’t break out.

labels=list(v_CA16_417="Movable Dwelling",                                  
            v_CA16_409="Single Detached",
            v_CA16_416="Other Single Attached",
            v_CA16_414="Duplex",
            v_CA16_412="Semi Detached",
            v_CA16_413="Row House",
            v_CA16_415="Apartment, < 5 storeys",
            v_CA16_410="Apartment, >= 5 storeys")
categories=c("Non-primary Residence",as.character(labels))
colors=setNames(
  c("#cccccc","#7a0177", "#ffff00", "#ffcc00", "#ff6600", "#ff0066", "#ff00ff", "#4dff4a", "#00ffff"),
  categories)
prep_data <- function(geo){
  data <- geo@data %>% replace(is.na(.), 0)
  names(data)[names(data) %in% names(labels)] <- as.character(labels[names(data)[names(data) %in% names(labels)]])
  data <- data %>% 
    mutate(`Non-primary Residence`=Dwellings - Households)
  geo@data <- data
  return(geo)
}

Next we grab the data via cancensus,

data_csd=get_census(dataset = dataset, regions=regions,vectors=vectors,geo_format='sp',labels='short',level='CSD') %>% prep_data

OGR data source with driver: GeoJSON 
Source: "data_cache/CM_geo_d0c52f6e04a54d8daf08bfeb51a22159.geojson", layer: "OGRGeoJSON"
with 1 features
It has 11 fields

data_ct=get_census(dataset = dataset, regions=regions,vectors=vectors,geo_format='sp',labels='short',level='CT') %>% prep_data

OGR data source with driver: GeoJSON 
Source: "data_cache/CM_geo_f8e7bafae9c5a7b33076dfc4ba182db3.geojson", layer: "OGRGeoJSON"
with 572 features
It has 11 fields

data_da=get_census(dataset = dataset, regions=regions,vectors=vectors,geo_format='sp',labels='short',level='DA') %>% prep_data

OGR data source with driver: GeoJSON 
Source: "data_cache/CM_geo_f1e81fc534b1253bc9e70561e853ce5a.geojson", layer: "OGRGeoJSON"
with 3702 features
It has 10 fields

data_db=get_census(dataset = dataset, regions=regions,geo_format='sp',labels='short',level='DB')

OGR data source with driver: GeoJSON 
Source: "data_cache/CM_geo_56d9a7d040f64a6a9bf50c93a44cfce3.geojson", layer: "OGRGeoJSON"
with 12622 features
It has 10 fields

which we then re-aggregate to make sure we don’t miss overall counts due to privacy cutoffs distribute them proportionally among the population.

data_ct@data <- dot_density.proportional_re_aggregate(data=data_ct@data,parent_data=data_csd@data,geo_match=setNames("GeoUID","CSD_UID"),categories=categories,base="Dwellings")
data_da@data <- dot_density.proportional_re_aggregate(data=data_da@data,parent_data=data_ct@data,geo_match=setNames("GeoUID","CT_UID"),categories=categories,base="Dwellings")

restarting interrupted promise evaluation

data_db@data <- dot_density.proportional_re_aggregate(data=data_db@data,parent_data=data_da@data,geo_match=setNames("GeoUID","DA_UID"),categories=categories,base="Dwellings")

Map

All that’s left to do is to covert our re-aggregated block-level data to dots, using the dot_density.compute_dots function from the dotdensity package and feed it into the dot_density.dots_map function to add them to our basemap.

# 1 dot = x dwelling units
scale=10
#dots.db <- dot_density.compute_dots(geo_data = data_db, categories = categories, scale=scale)
basemap +
  # zoom in a bit
  #coord_fixed(xlim=c(-123.29,-122.6), ylim=c(49.02,49.35), ratio = 1/cos(49.2/180*pi)) +
  scale_colour_manual(values = colors) +
  labs(color = "",
                title="Dwelling Units 2016",
                caption="Source: StatCan Census 2016 via cancensus & CensusMapper.ca",
                subtitle = paste0("1 dot = ",scale," dwelling units")) + 
  dot_density.dots_map(dots=dots.db,alpha=0.9,size=0.25)

# save image for later
ggsave('../images/dwellings.png',width=26,height=26)